Wyniki wyszukiwania dla: INSTANCE SEGMENTATION,SYNTHETIC DATASET,DEEP LEARNING
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Precise Identification of Different Cervical Intraepithelial Neoplasia (CIN) Stages, Using Biomedical Engineering Combined with Data Mining and Machine Learning
PublikacjaCervical cancer (CC) is one of the most common female cancers worldwide. It remains a significant global health challenge, particularly affecting women in diverse regions. The pivotal role of human papillomavirus (HPV) infection in cervical carcinogenesis underscores the critical importance of diagnostic strategies targeting both HPV infection and cervical...
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A Perspective on Missing Aspects in Ongoing Purification Research towards Melissa officinalis
PublikacjaMelissa officinalis L. is a medicinal plant used worldwide for ethno-medical purposes. Today, it is grown everywhere; while it is known to originate from Southern Europe, it is now found around the world, from North America to New Zealand. The biological properties of this medicinal plant are mainly related to its high content of phytochemical (bioactive) compounds, such as flavonoids, polyphenolic compounds, aldehydes, glycosides...
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Microscale microbial fuel cell (MFC) batch experiment results
Dane BadawczeThis dataset provides the results of six different scenarios meant to assess the efficiency COD, TN, TP removal and voltage generation in microbial fuel cells (MFC).
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Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning
PublikacjaTheophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical solubility measured at 25 °C in a broad range of solvents, including neat, binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water. There was a strong synergistic effect of organic solvents mixed with water, and among the experimentally...
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Impact of AlphaFold on structure prediction of protein complexes: The CASP15‐CAPRI experiment
PublikacjaWe present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody–antigen complexes, and 7 large assemblies. On average 70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups...
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ARIMA vs LSTM on NASDAQ stock exchange data
PublikacjaThis study compares the results of two completely different models: statistical one (ARIMA) and deep learning one (LSTM) based on a chosen set of NASDAQ data. Both models are used to predict daily or monthly average prices of chosen companies listed on the NASDAQ stock exchange. Research shows which model performs better in terms of the chosen input data, parameters and number of features. The chosen models were compared using...
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The High Quality Business School Academic Teacher of the 21st Century – Polish Students’ Perspective
PublikacjaThe literature shows that the success and competence of future managers depend on the quality of their academic teachers. Moreover high quality study requires high quality lecturing/teaching that creates an environment in which deep learning outcomes are made possible for students. The aim was to identify the characteristics of the academic teacher working at business schools, according to the expectations of Polish students...
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Selection of material for infiltration of non-stoichiometric Sr0.95Ti0.3Fe0.7O3-δ
Dane BadawczeThe porous electrodes used for the infiltration were prepared from A-site non-stoichiometric Sr0.95Ti0.3Fe0.7O3-δ synthesized by the conventional solid state reaction method. Screen printed electrodes (0.5 cm2) on CGO-20 substrate, were fired at 800 °C for 2 hours in an air atmosphere. Infiltration of the scaffolds was performed by a water-based precursor...
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Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series
PublikacjaThis study assessed the usefulness of algorithms in estimating energy consumption and carbon dioxide emissions in Viet- nam, in which the training dataset was used to train the models linear regression, random forest, XGBoost, and AdaBoost, allowing them to comprehend the patterns and relationships between population, GDP, and carbon dioxide emissions, energy consumption. The results revealed that random forest, XGBoost, and AdaBoost...
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Predicting seismic response of SMRFs founded on different soil types using machine learning techniques
PublikacjaPredicting the Maximum Interstory Drift Ratio (M-IDR) of Steel Moment-Resisting Frames (SMRFs) is a useful tool for designers to approximately evaluate the vulnerability of SMRFs. This study aims to explore supervised Machine Learning (ML) algorithms to build a surrogate prediction model for SMRFs to reduce the need for complex modeling. For this purpose, twenty well-known ML algorithms implemented in Python software are trained...
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Machine learning-based seismic response and performance assessment of reinforced concrete buildings
PublikacjaComplexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data...
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CTD Gdańsk Deep_2001_2005
Dane BadawczeDataset includes measurements of conductivity (mS cm-1), temperature (°C), sound speed (m s-1) and salinity (PSU) made with the probe Falmouth Scientific Inc. The research was carried out in 2001 (at the turn of May and June), 2003 (beginning of May), 2005 (end of April) and in 2002 and 2004 (at the turn of September and October). In 2002, only temperature...
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How personality traits, sports anxiety, and general imagery could influence the physiological response measured by SCL to imagined situations in sports?
Dane BadawczeThe data were collected to understand how individual differences in personality (e.g. neuroticism), general imagery, and situational sports anxiety are linked to arousal measuring with skin conductance level (SCL) in situational imagery (as scripted for sport-related scenes). Thirty persons participated in the study, aged between 14 and 42 years, with...
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Hydrodynamic reanalysis of currents in the Baltic Sea using the PM3D model
Dane BadawczeThe dataset contains the results of numerical modeling of currents in the Baltic Sea since 1998. A long-term reanalysis was performed using a three-dimensional hydrodynamic model PM3D (Kowalewski and Kowalewska-Kalkowska, 2017), a new version of the M3D model (Kowalewski, 1997).
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Computationally Efficient Surrogate-Assisted Design of Pyramidal-Shaped 3D Reflectarray Antennas
PublikacjaReflectarrays (RAs) have been attracting considerable interest in the recent years due to their appealing features, in particular, a possibility of realizing pencil-beam radiation patterns, as in the phased arrays, but without the necessity of incorporating the feeding networks. These characteristics make them attractive solutions, among others, for satellite communications or mobile radar antennas. Notwithstanding, available microstrip...
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Sampling-based novel heterogeneous multi-layer stacking ensemble method for telecom customer churn prediction
PublikacjaIn recent times, customer churn has become one of the most significant issues in business-oriented sectors with telecommunication being no exception. Maintaining current customers is particularly valuable due to the high degree of rivalry among telecommunication companies and the costs of acquiring new ones. The early prediction of churned customers may help telecommunication companies to identify the causes of churn and design...
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Ocean mixed layer dynamics: high-resolution simulations of wind, wave and convective effects
Dane BadawczeThis dataset contains results of high-resolution numerical simulations of the ocean mixed layer (OML) forced by wind, waves and cooling from the atmosphere, i.e., under strongly turbulent, convective conditions. The goal is to provide detailed, three-dimensional information about OML circulation, turbulent kinetic energy, and temperature and salinity...
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Buried Object Characterization by Data-Driven Surrogates and Regression-Enabled Hyperbolic Signature Extraction
PublikacjaThis work addresses artificial-intelligence-based buried object characterization using FDTD-based electromagnetic simulation toolbox of a Ground Penetrating Radar (GPR) to generate B-scan data. In data collection, FDTD-based simulation tool, gprMax is used. The task is to estimate geophysical parameters of a cylindrical shape object of various radii, buried at different positions in the dry soil medium simultaneously and independently...
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Long-term hindcast simulation of currents in the Baltic Sea
Dane BadawczeThe dataset contains the results of numerical modelling of currents over a period of 50 years (1958-2007) in the Baltic Sea . A long-term hindcast simulation was performed using a three-dimensional hydrodynamic model PM3D (Kowalewski and Kowalewska-Kalkowska, 2017), a new version of the M3D model (Kowalewski, 1997). The hydrodynamic model was coupled...